{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1  162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2  162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3  162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4  162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2017-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2017-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2017-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2017-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_table('data/log.txt',sep='\t',header = None,\n",
    "                     names = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at'])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.检查是否有重复值，空值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "重复值检查结果:\n",
      "0\n",
      "空值检查结果:\n",
      "id              0\n",
      "api             0\n",
      "count           0\n",
      "res_time_sum    0\n",
      "res_time_min    0\n",
      "res_time_max    0\n",
      "res_time_avg    0\n",
      "interval        0\n",
      "created_at      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print('重复值检查结果:')\n",
    "print(data.duplicated(subset='created_at').sum())\n",
    "print('空值检查结果:')\n",
    "print(data.isnull().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结果:没有重复值，没有空值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.检查是否有异常值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "异常检查:\n",
      "Step1 : 查看describe\n",
      "Step1结果: 明显可以看出 Max values 有很大的偏差，数据中存在异常值\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.794960e+05</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.000000</td>\n",
       "      <td>179496.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.866490e+06</td>\n",
       "      <td>7.175909</td>\n",
       "      <td>1393.177370</td>\n",
       "      <td>108.419620</td>\n",
       "      <td>359.880351</td>\n",
       "      <td>187.812208</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.686579e+06</td>\n",
       "      <td>4.325160</td>\n",
       "      <td>1499.485881</td>\n",
       "      <td>79.640559</td>\n",
       "      <td>638.919769</td>\n",
       "      <td>224.464813</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.625420e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>36.550000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.825183e+06</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>607.707500</td>\n",
       "      <td>83.410000</td>\n",
       "      <td>198.280000</td>\n",
       "      <td>144.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.811432e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1154.905000</td>\n",
       "      <td>97.120000</td>\n",
       "      <td>256.090000</td>\n",
       "      <td>167.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.981397e+06</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1834.117500</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>374.410000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.343909e+07</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>142650.550000</td>\n",
       "      <td>18896.640000</td>\n",
       "      <td>142468.270000</td>\n",
       "      <td>71325.000000</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 id          count   res_time_sum   res_time_min  \\\n",
       "count  1.794960e+05  179496.000000  179496.000000  179496.000000   \n",
       "mean   6.866490e+06       7.175909    1393.177370     108.419620   \n",
       "std    3.686579e+06       4.325160    1499.485881      79.640559   \n",
       "min    1.625420e+05       1.000000      36.550000       3.210000   \n",
       "25%    3.825183e+06       4.000000     607.707500      83.410000   \n",
       "50%    6.811432e+06       7.000000    1154.905000      97.120000   \n",
       "75%    9.981397e+06      10.000000    1834.117500     116.990000   \n",
       "max    1.343909e+07      31.000000  142650.550000   18896.640000   \n",
       "\n",
       "        res_time_max   res_time_avg  interval  \n",
       "count  179496.000000  179496.000000  179496.0  \n",
       "mean      359.880351     187.812208      60.0  \n",
       "std       638.919769     224.464813       0.0  \n",
       "min        36.550000      36.000000      60.0  \n",
       "25%       198.280000     144.000000      60.0  \n",
       "50%       256.090000     167.000000      60.0  \n",
       "75%       374.410000     202.000000      60.0  \n",
       "max    142468.270000   71325.000000      60.0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('异常检查:')\n",
    "print('Step1 : 查看describe')\n",
    "print('Step1结果: 明显可以看出响应时间的 Max values 有很大的偏差，数据中存在异常值')\n",
    "data.describe()\n",
    "#plt.boxplot(data['count'],whis = 3)\n",
    "#plt.show()\n",
    "#plt.boxplot(data['res_time_min'],whis = 150)\n",
    "#plt.show()\n",
    "#plt.boxplot(data['res_time_max'],whis = 150)\n",
    "#plt.show()\n",
    "#data2 = data.drop(index = data['res_time_avg'].idxmax())\n",
    "#data2 = data.drop(index = data['res_time_avg'].idxmax())\n",
    "#data2.describe()\n",
    "#print(data[(np.abs(data['res_time_avg'] * data['count'] - data['res_time_sum'])/data['count'] > 2)])\n",
    "#print(data[(data['res_time_sum'] < data['res_time_min'] * data['count']) | (data['res_time_sum'] > data['res_time_max'] * data['count'])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step2 : 检查 \"count\" \"res_time_avg\" \"res_time_sum\" \"res_time_min\" \"res_time_max\" 这几列的相互值是否正常\n",
      "Step2 结果: 结果正常\n",
      "Empty DataFrame\n",
      "Columns: [id, api, count, res_time_sum, res_time_min, res_time_max, res_time_avg, interval, created_at]\n",
      "Index: []\n",
      "Empty DataFrame\n",
      "Columns: [id, api, count, res_time_sum, res_time_min, res_time_max, res_time_avg, interval, created_at]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "print('Step2 : 检查 \"count\" \"res_time_avg\" \"res_time_sum\" \"res_time_min\" \"res_time_max\" 这几列的值是否符合现实逻辑')\n",
    "print('Step2 结果: 符合现实逻辑，结果正常')\n",
    "print(data[(np.abs(data['res_time_avg'] * data['count'] - data['res_time_sum'])/data['count'] > 2)])\n",
    "print(data[(data['res_time_sum'] < data['res_time_min'] * data['count']) | (data['res_time_sum'] > data['res_time_max'] * data['count'])])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Step3 : 检查\"res_time_avg\" 列，规定 k值(whis)为200 做初步检查\n",
      "Step3 结果: k值200是一个非常大的值，但从图中可以看出\n",
      "58.0\n",
      "11802.0\n",
      "-11456.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>76009</th>\n",
       "      <td>5897714</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>44068.25</td>\n",
       "      <td>5872.03</td>\n",
       "      <td>24626.21</td>\n",
       "      <td>14689.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-01-28 17:54:45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100808</th>\n",
       "      <td>7480534</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>7</td>\n",
       "      <td>83667.58</td>\n",
       "      <td>2376.46</td>\n",
       "      <td>28236.13</td>\n",
       "      <td>11952.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-03-01 20:33:42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121056</th>\n",
       "      <td>8956594</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>10</td>\n",
       "      <td>140691.03</td>\n",
       "      <td>109.77</td>\n",
       "      <td>101779.53</td>\n",
       "      <td>14069.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-03-25 14:42:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121064</th>\n",
       "      <td>8957159</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>2</td>\n",
       "      <td>142650.55</td>\n",
       "      <td>182.28</td>\n",
       "      <td>142468.27</td>\n",
       "      <td>71325.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-03-25 14:50:10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139214</th>\n",
       "      <td>10329399</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>1</td>\n",
       "      <td>18896.64</td>\n",
       "      <td>18896.64</td>\n",
       "      <td>18896.64</td>\n",
       "      <td>18896.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-04-15 12:07:32</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id                     api  count  res_time_sum  res_time_min  \\\n",
       "76009    5897714  /front-api/bill/create      3      44068.25       5872.03   \n",
       "100808   7480534  /front-api/bill/create      7      83667.58       2376.46   \n",
       "121056   8956594  /front-api/bill/create     10     140691.03        109.77   \n",
       "121064   8957159  /front-api/bill/create      2     142650.55        182.28   \n",
       "139214  10329399  /front-api/bill/create      1      18896.64      18896.64   \n",
       "\n",
       "        res_time_max  res_time_avg  interval           created_at  \n",
       "76009       24626.21       14689.0        60  2018-01-28 17:54:45  \n",
       "100808      28236.13       11952.0        60  2018-03-01 20:33:42  \n",
       "121056     101779.53       14069.0        60  2018-03-25 14:42:10  \n",
       "121064     142468.27       71325.0        60  2018-03-25 14:50:10  \n",
       "139214      18896.64       18896.0        60  2018-04-15 12:07:32  "
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('Step3 : 检查\"res_time_avg\" 列，规定 k值(whis)为200 做初步检查')\n",
    "print('Step3 结果: k值200是一个较大的值，但从图中可以看出还是有一个点有很大的偏差，所有可以断定数据中有异常值。')\n",
    "k = 200\n",
    "val = data['res_time_avg'].quantile(0.75)-data['res_time_avg'].quantile(0.25)\n",
    "print(val)\n",
    "max_value =  data['res_time_avg'].quantile(0.75) +  val * k\n",
    "print(max_value)\n",
    "min_value =  data['res_time_avg'].quantile(0.25) - val * k\n",
    "print(min_value)\n",
    "plt.boxplot(data['res_time_avg'],whis = 200)\n",
    "# data2 = \n",
    "plt.show()\n",
    "data[(data['res_time_avg'] > max_value)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结果:响应时间存在异常值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.分析api和interval列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"api\"和\"interval\"是常量，对分析用处不大。在标题中有记录的情况下可以丢弃。\n",
      "['/front-api/bill/create']\n",
      "[60]\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2017-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2017-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2017-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2017-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2017-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  162542      8       1057.31         88.75        177.72         132.0   \n",
       "1  162644      5        749.12        103.79        240.38         149.0   \n",
       "2  162742      5        845.84        136.31        225.73         169.0   \n",
       "3  162808      9       1305.52         90.12        196.61         145.0   \n",
       "4  162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2017-11-01 00:00:07  \n",
       "1  2017-11-01 00:01:07  \n",
       "2  2017-11-01 00:02:07  \n",
       "3  2017-11-01 00:03:07  \n",
       "4  2017-11-01 00:04:07  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('\"api\"和\"interval\"是常量，对分析用处不大。在标题中有记录的情况下可以丢弃。')\n",
    "print(data['api'].unique())\n",
    "print(data['interval'].unique())\n",
    "data.drop(columns = ['api','interval'],inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 结果:\"api\"和\"interval\"是常量，对分析用处不大。在标题中有记录的情况下可以丢弃。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.使用 created_at作为索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>created_at</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:00:07</th>\n",
       "      <td>0</td>\n",
       "      <td>162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:01:07</th>\n",
       "      <td>1</td>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:02:07</th>\n",
       "      <td>2</td>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:03:07</th>\n",
       "      <td>3</td>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-11-01 00:04:07</th>\n",
       "      <td>4</td>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     index      id                     api  count  \\\n",
       "created_at                                                          \n",
       "2017-11-01 00:00:07      0  162542  /front-api/bill/create      8   \n",
       "2017-11-01 00:01:07      1  162644  /front-api/bill/create      5   \n",
       "2017-11-01 00:02:07      2  162742  /front-api/bill/create      5   \n",
       "2017-11-01 00:03:07      3  162808  /front-api/bill/create      9   \n",
       "2017-11-01 00:04:07      4  162943  /front-api/bill/create      3   \n",
       "\n",
       "                     res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "created_at                                                                    \n",
       "2017-11-01 00:00:07       1057.31         88.75        177.72         132.0   \n",
       "2017-11-01 00:01:07        749.12        103.79        240.38         149.0   \n",
       "2017-11-01 00:02:07        845.84        136.31        225.73         169.0   \n",
       "2017-11-01 00:03:07       1305.52         90.12        196.61         145.0   \n",
       "2017-11-01 00:04:07        568.89        138.45        232.02         189.0   \n",
       "\n",
       "                     interval  \n",
       "created_at                     \n",
       "2017-11-01 00:00:07        60  \n",
       "2017-11-01 00:01:07        60  \n",
       "2017-11-01 00:02:07        60  \n",
       "2017-11-01 00:03:07        60  \n",
       "2017-11-01 00:04:07        60  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.reset_index()\n",
    "data = data.set_index('created_at')\n",
    "#data = data.interpolate()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['created_at'] = pd.to_datetime(data['created_at'],format=\"%Y-%m-%d %H:%M:%S\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6.分析api调用情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = data[['count']]\n",
    "#data2 = data2.interpolate()\n",
    "data2.plot()\n",
    "x_axis = pd.date_range('2017-11-01',periods = 2, freq = 'D')\n",
    "#print(x_axis[0])\n",
    "#print(x_axis[-1])\n",
    "plt.axis([x_axis[0],x_axis[-1],0,20])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7.分析一天中API响应时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = data[['res_time_min','res_time_max','res_time_avg']].resample('15T').mean()\n",
    "#data2 = data2.fillna(method='ffill')\n",
    "data2.plot()\n",
    "x_axis = pd.date_range('2017-11-10',periods = 2, freq = 'D')\n",
    "#print(x_axis[0])\n",
    "#print(x_axis[-1])\n",
    "plt.axis([x_axis[0],x_axis[-1],0,400])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8.分析连续几天业务访问次数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function matplotlib.pyplot.show(*args, **kw)>"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = data[['count']]#.resample('10T').mean()\n",
    "data2.plot()\n",
    "x_axis = pd.date_range('2017-11-01',periods = 10, freq = 'D')\n",
    "#print(x_axis[0])\n",
    "#print(x_axis[-1])\n",
    "plt.axis([x_axis[0],x_axis[-1],0,33])\n",
    "plt.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.分析周末和非周末访问情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x17622650>]"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data3 = data[(data.created_at >= '2018-05-26 00:00:00') & (data.created_at <= '2018-05-26 23:59:59')].copy()\n",
    "data3 = data['2018-05-26 00:00:00':'2018-05-26 23:59:59'].copy()\n",
    "data3 = data3[['count']].resample('20T').mean()\n",
    "data3 = data3.reset_index()\n",
    "data3['created_at'] = data3['created_at'].dt.time\n",
    "data3 = data3.interpolate()\n",
    "plt.plot(data3['created_at'],data3['count'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1765f4f0>]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#data2 = data[(data.created_at >= '2018-05-25 00:00:00') & (data.created_at <= '2018-05-25 23:59:59')].copy()\n",
    "data2 = data['2018-05-25 00:00:00':'2018-05-25 23:59:59'].copy()\n",
    "#data2 = data2.set_index('created_at')\n",
    "data2 = data2[['count']].resample('20T').mean()\n",
    "data2 = data2.reset_index()\n",
    "data2['created_at'] = data2['created_at'].dt.time\n",
    "data2 = data2.interpolate()\n",
    "plt.plot(data2['created_at'],data2['count'])\n",
    "#data2['created_at'] = pd.to_datetime(data2['created_at'].dt.time,format=\"%H:%M:%S\")\n",
    "#type(data2['created_at'])\n",
    "#data2 = data2.set_index('created_at')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data2['created_at'],data2['count'],linestyle = '-',label = 'False')\n",
    "plt.plot(data3['created_at'],data3['count'],linestyle = '-.',label = 'True')\n",
    "plt.legend(title = 'Weekend')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
